Adaptive navigation technique for navigating a catheter through a body channel or cavity

ABSTRACT

A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 60/865,379 filed Nov. 10, 2006 entitled Adaptive Navigation Method;U.S. Provisional Application Ser. No. 60/867,428 filed Nov. 28, 2006entitled Adaptive Navigation Technique For Navigating A Catheter ThroughA Body Channel Or Cavity; and U.S. Provisional Application Ser. No.60/887,663 filed Feb. 1, 2007 entitled Adaptive Navigation Technique ForNavigating A Catheter Through A Body Channel Or Cavity, all of which arehereby incorporated by reference.

BACKGROUND OF THE INVENTION

Breakthrough technology has emerged which allows the navigation of acatheter tip through a tortuous channel, such as those found in thepulmonary system, to a predetermined target. This technology comparesthe real-time movement of a locatable guide (LG) against athree-dimensional digital map of the targeted area of the body (forpurposes of explanation, the pulmonary airways of the lungs will be usedhereinafter, though one skilled in the art will realize the presentinvention could be used in any body cavity or system: circulatory,digestive, pulmonary, to name a few).

Such technology is described in U.S. Pat. Nos. 6,188,355; 6,226,543;6,558,333; 6,574,498; 6,593,884; 6,615,155; 6,702,780; 6,711,429;6,833,814; 6,974,788; and 6,996,430, all to Gilboa or Gilboa et al.; andU.S. Published Applications Pub. Nos. 2002/0193686; 2003/0074011;2003/0216639; 2004/0249267 to either Gilboa or Gilboa et al. All ofthese references are incorporated herein in their entireties.

One aspect of this background technology pertains to the registration ofthe CT images that were used, collectively, as a three-dimensionaldigital map against the actual movement of the LG through the pulmonarysystem. The user interface shows three separate CT-based imagesreconstructed by software from x, y, and z directions, simultaneouslywith the LG location superimposed onto the intersection point of thereconstructed images. If the CT images do not accurately reflect theactual location of the airways, the LG will quickly appear to drift outof the airways as the LG is advanced, thereby diminishing the utility ofthe navigation system.

Presently, registration points at chosen known landmarks in the centralarea of lungs are used to register or align the CT based digital mapwith the patient's chest cavity. These registrations points are firstchosen during a planning stage and marked on the internal lung surface.At the beginning of the procedure, the corresponding points are touchedand recorded using the LG aided by a bronchoscope in the patient'sairways. Doing so allows a computer to align the digital map with thedata received from the LG such that an accurate representation of theLG's location is displayed on a monitor.

However, due to various factors, the accuracy of the registrationdiminishes as the distance between LG and the registration pointsincreases. In other words, the navigation system is less accurate at theperiphery of the lungs, where it is most needed. This is due to variousfactors, two of which are the focus of the present invention. The firstfactor involves the rigidity of the CT digital image utilized as adigital map by current system while the lung structure is flexible.Second, as the distance increases from the last registration point,errors compound. Compounded errors, coupled with the flexible airways,result in LG that appear to be outside of the airways on the CT images.

As a result of the accumulative inaccuracies, the performance of theexisting system is limited. For example, once the bronchoscope is toobig to advance, the existing system provides guidance to the user as towhether the LG is being advanced in the direction of the target ignoringthe inaccuracies created by the flexibility and internal movement of theliving airways. In addition, the guidance instructions to the target aregiven without regard to the geometries of the airways leading to thetarget. As a result, user gently advances the LG and watches whether theLG is moving in the direction of the target. If it is not, the LG isretracted and the user “feels” for another airway that may lead to thetarget rather than see it directly on the CT cross-sections. Hence, twoproblems arise. First, the LG no longer appears to be located within theairways. Second, the guidance provided does not guide the user along alogical path, it merely provides a general direction to the lesion.

The present invention addresses these two issues by using a uniquealgorithm to create a BT skeleton, which is a three-dimensional virtualmap of the bronchial airways, and by continuously and adaptivelymatching the LG path to the BT skeleton. Due to the increased accuracyof the BT skeleton and the registration, three-dimensional guidance isextended past the limits of the bronchoscope.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphic representation of an algorithm of the presentinvention;

FIG. 2 is a table showing relative importance of sources of erroruncertainty encountered in a method of the present invention;

FIG. 3 is a chart showing steps of the pathway generation process of thepresent invention;

FIGS. 4-13 are screen captures of the user interface during the pathwaygeneration process of the present invention; and

FIG. 14 is a screen capture of the user interface of the presentinvention while a procedure is being performed on a patient.

DETAILED DESCRIPTION OF THE INVENTION Method of Generating a Pathway toa Target in the Lungs

The present invention includes a unique method of generating a BTskeleton such that an accurate and logical pathway to the target may beformed. Generally, this method begins with an algorithm thatautomatically detects the trachea inside the CT volume, athree-dimensional image created from a plurality of CT scans, and usesthis as a starting point for the generation of the BT. Next, a differentsegmentation step is applied to mark those voxels of the CT scan thatrepresent air inside the bronchi. Next, the segmented and filtered datais skeletonized—center lines of the perceived airways are defined andused to build an anatomically valid virtual model of the airways.

More specifically, the method of generating a pathway to a target insidethe lungs is outlined as follows:

1. Bronchial Tree Generation

Bronchial tree generation is a fully automatic process that runs in thebackground and is thus transparent to the user while working with theapplication software.

2. Automatic Seed Point Detection

Automatic seed point detection is an algorithm that detects the tracheaby searching for a tubular object having the density of air in the upperregion of the CT volume. The center of gravity of the found tubularobject is defined as a seed point for further segmentation.

3. Segmentation: Lung's Air Differentiation

Segmentation is a process based on a Region Growing Algorithm (see p. 73of Handbook of Medical Imaging, Processing and Analysis, Isaac N.Bankman, Academic Press, 2000, incorporated by reference herein in itsentirety.), which defines and displays the bronchial airways from the CTvolume images of the human chest. The purpose of the Region GrowingAlgorithm is to construct homogeneous regions of points connected to thestarting seed point and satisfying the following condition: Hounsfieldvalues (HU) in all of these points are lower than a predefined maximumthreshold value.

The implemented process is fully automatic, iterative and consists ofseveral steps:

3.1 Anatomical feature segmentation:

The purpose is to mark (or segment) the portion of voxels (voxel=VolumePixel) of a substance inside a recognizable feature of the lumen networkThis recognizable feature is used as a starting point. For example, inthe case of the airways of the lungs being the lumen network, thetrachea is preferred for selection as the anatomical feature. Hence theportion of voxels representing air inside the trachea, avoiding thebubbles caused by noise and artifacts inside the CT images is marked.Region growing with a high threshold value is then applied inside thevolume for this purpose. If the blood vessels constitute the lumennetwork of interest, for example, the aorta could be used as theanatomical feature.

3.2 Adaptive threshold detection for region growing algorithm:

3.2.1 Starting from the boundary of the previously segmented area fromstep 3.1, multiple iterations of a region growing algorithm areperformed. With each iteration the following steps occur:

3.2.1.1 A threshold value is defined and all voxels lower than thethreshold value are deemed to be containing only air and are thussegmented. This process is iterative, but growth rate and geometry arenot considered.

3.2.1.2 After the segmentation process is completed for that iteration,the whole number of segmented voxels inside the lungs stemming from theseed point are recorded.

3.2.2 Next the threshold value is increased and the next iteration isperformed. After this iteration is completed, the number of segmentedvoxels between the current iteration and the previous one are compared.

3.2.3 Each iteration should result in a greater number of connectedvoxels because the threshold value increases with each iteration.Increasing the threshold value means that more voxels are considered asair.

3.2.4 If the difference in the number of segmented voxels between twoconsecutive iterations has increased significantly, this event isconsidered as leakage. Practically it means that somewhere the bronchiwall was “broken” by segmentation and in addition to the air inside thelung, the outside lung air is now connected to the segmented volume. Soa conclusion is drawn that the current threshold is too high and thethreshold value from the previous iteration is used.

3.2.5 Finally segmentation is performed with the selected threshold.This time the segmentation result is added to the step 1 and stored.This will be used as a starting point for the next step.

3.3 Leakage control:

This is required in order to improve the results of the region-growingalgorithm using the adaptive threshold local values by segmentingadditional areas. The technique of section 3.2 is applied for everyboundary point (point located on tissue) of previously segmented area.

3.4 Geometry control wave propagation:

This is described in the article, “Hybrid Segmentation and Explorationof the Human Lungs, IEEE Visualization 2003, Dirk Bartz, Dirk Mayer, JanFischer, Sebastian Ley, Anxo del Ro, Stef Thust, Claus Peter Heussel,Hans-Ulrich Kauczor, and Wolfgang StraBer, the entirety of which isincorporated by reference herein. This article enables additionalimprovement over previous steps using higher threshold levels due amechanism of geometrical parameter control of growing branches.

3.5 “Template matching”:

This approach is based on the aforementioned article by Bartz et al. andevaluates the candidate area below templates with the values ofuncertain density (between −950 HU and −775 HU). This is organized intwo stages; the first stage establishes templates that are used in thesecond stage to evaluate the local voxel neighborhood. First, 2Dtemplate matching applies 2D region growing starting from the boundaryvoxels of the previous segmentations. The thresholds are varied—from theupper threshold of the uncertain density value interval (−775 HU)—untilthe number of selected voxels is below the critical limit, since it canbe assumed that they did not leak out. Based on this selected voxelarea, circular templates of varying sizes is generated. In the secondstage, we apply a 2D region growing. The shape of each connectedsegmented area is compared with set of circular templates from the 1ststage. The positive comparison result is then selected and added to thesegmentation.

3.6 Bubble filter:

Finally a bubble filter is applied. A bubble filter is a combination ofmorphological dilation and erosion operations. It is used to eliminatesmall non-segmented regions (bubbles) from the final segmented area.These bubbles appear due to the noisy nature and artifacts of CT images.

4. Skeletonization and Feature Calculation

Skeletonization and feature calculation refers to the extraction ofcenterlines of previously segmented bronchi, the building of a validanatomical hierarchy of bronchial airways, the calculation of bronchidiameters and geometric features, and the surface generation for eachsegmented bronchi. The following steps are involved:

4.1 Thinning algorithm:

The iterative object reduction technique described in the article, ASequential 3D Thinning Algorithm and Its Medical Applications, K'alm'anPal'agyi, Erich Sorantin, Emese Balogh, Attila Kuba, Csongor Halmai,Bal'azs Erd″ohelyi, and Klaus Hausegger, 17th Int. Conf. IPMI (2001)409-415 the entirety of which is incorporated herein by reference, andis used to convert the previously segmented airways into a geometricskeleton representation.

4.2 Branches and node points detection:

A map of all the skeleton voxels is generated, so for each voxel we havea list of neighbor voxels. Voxels with three or more neighbors areconsidered to be “node points”. The voxels with two neighbors areconsidered as points on the branch. The entire voxel map is rearrangedas a graph with nodes and branches.

4.3 Filtering of false branches:

This involves the following steps:

4.3.1 Identify and remove disconnected branches.

4.3.2 Resolve graph loops by removing the longest branch of two branchesconnected to a common node.

4.3.3 Remove relatively short leaves in the graph, considering them aresult of a leakage.

4.3.4 Remove leaves that are relatively close to each other.

4.4 Convert graph to tree:

Find the root point on the graph as one nearest to the seed point foundin 1. The graph is converted to a binary tree. Branches are approximatedby polynomials.

4.5 Branch labeling: logical and hierarchical:

This is performed according to the technique described in the article,Automated Nomenclature Labeling of the Bronchial Tree in 3D-CT LungImages, Hiroko Kitaoka from Osaka University, Yongsup Park , JuergTschirren, Joseph Reinhardt, Milan Sonka, Goeffrey McLennan, and Eric A.Hoffman from University of Iowa, Lecture Notes in Computer Science, T.Dohi and R. Kikinis, Eds. Amsterdam, The Netherlands: Springer-Verlag,Oct. 2002, vol. 2489, pp. 1-11, the entirety of which is incorporated byreference herein.

4.6 Automatic evaluation of tree quality

Tree quality is evaluated based on the recognition of the following mainparts of the skeleton:

4.6.1 right lower lobe (RLL) and right middle lobe (RML),

4.6.2 right upper lobe (RUL)

4.6.3 left upper lobe (LUL)

4.6.4 left low lobe (LLL)

Branch numbers and branch length features are calculated separately foreach area and compared to statistical model or template of acceptableanatomy to evaluate the tree quality.

4.7 Extraction of External surfaces of bronchial tubes:

Modification of a widely known method called “marching cubes” is used toextract airways surface from volumetric CT data.

5. Planning the Path to the Peropheral Target

This process plans the pathway from the trachea entrance to the targetarea.

As the CT resolution limits the final quality of automatically generatedbronchial tree described in 1., the user is enabled to perform thepathway fine tuning.

6. Target Marking

The planning software is used for planning the bronchoscopic procedureof navigating to suspect lesion (target) inside the human lungs.

The target center and target dimensions are manually marked with theplanning software.

7. Pathway Semi-Automatic Generation

At this point there are both the automatically generated bronchial treeand the target. However the target may be located out of the tree. Thishappens for several reasons, including:

-   -   The target may lay inside the tissue    -   Some small bronchi may be missing from the automatically        generated tree due to the CT resolution limitations.

Therefore, a gap is created and shall be completed manually. Using boththe interactive display of the bronchial tree and CT cross-sections, theuser manually selects the point on the bronchial tree that shall beconnected to the target center. This is called the “exit point”.

The pathway from the trachea to the “exit point” is automaticallygenerated. In addition the original tree is extended by a linear branchthat connects the “exit point” and the target center.

8. Pathway Fine Tuning

Using the CT cross-section user is optionally able to define split theautomatically created linear branch into segments, defining theintermediate waypoints by intuitive graphic user interface.

9. On-Path Guidance

On-Path guidance is designed to keep the locatable tool inside theplanned path (displayed in green). In this approach the path isapproximated by an automatically generated 3D poly-line. The poly-linesegments are connected with the vertex. Each vertex is defined as anintermediate target in our system. During navigation when anintermediate target is reached, it disappears and the next intermediatetarget appears and becomes the current target. The mathematical vectorconnecting the actual location of the locatable tool with the incomingintermediate target is calculated. This mathematical vector istranslated to the locatable tool operation through the followinginstructions set:

9.1 Push Forward\Backward

9.2 Set the specific rotation angle

9.3 Apply bending ON\OFF.

Additional methods are contemplated that may improve the accuracy of theBT generated by the aforementioned method. First, arterial blood vesselsmay be tracked and used to regenerate missing airway data from the CT.Because the arterial blood vessels from the heart to the lungs terminateat the alveoli, deductions can be made regarding the location of thebronchioles leading to the alveoli. Second, an anatomic atlas createdfrom data derived from multiple lung models can be used to evaluate andcomplete the generated BT geometry. Though every lung is unique, eachhas common characteristics portrayed in an anatomic atlas. Thisinformation can be used to deduce and fill in missing BT geometry data.

Accuracy may also be improved by utilizing multiple sensors. Forexample, acquiring the location and orientation data from theelectromagnetic system may be performed using multiple external and/ormultiple internal sensors. These could be located on the extendedworking channel (EWC), the locatable guide (LG), the bronchoscope, orattached to the interior of the lung.

The location and orientation data acquired from the electromagneticsystem, regardless of the number of sensors used, may be used tocomplete any missing branches from the BT due to limitation in CTresolution.

It is also contemplated that flexibility may be added to the generatedBT structure by utilizing multiple sets of CT data, each representingdifferent points in the patient's breathing cycle. For example, three CTscans could be taken, one at the peak inhalation point of a normalbreathing cycle, one at the peak exhalation point of a normal breathingcycle, and one midway in between. External sensor positions mayoptionally be noted to record chest positions during these various“snapshots” taken with the CT. Noting the differences in positions ofthe bronchial features in each of the three locations providesinformation on the individual movement paths of the features during thebreathing cycle. The movement paths can be estimated by connecting thethree recorded points. Once the flexible BT is generated, externalposition sensors on the patient can be used to detect the patient'sbreathing cycle, and for determining the corresponding locations of thevarious bronchial features along their respective movement paths.

This simulated flexibility can be calculated and used individually foreach patient or, if it is desired to minimize the cost and radiationexposure of multiple CT scans, can be used as a model for otherpatients. Several models can be recorded and kept on file for latermatching to patients as a function of anatomic location, patientdimension, gender age, phase of breathing cycle, etc.

Adaptive Navigation Method

The present invention also provides unique method of continually andadaptively matching the automatically generated BT skeleton to thepatient during the procedure. Generally, this method records consecutivelocations of the LG as it is advanced through the airways. Because it isknown that the LG travels through airways, the BT skeleton iscontinually matched such that the LG appears in an airway. Hence, theaccuracy of the navigation improves, rather than degrades, as the LG isadvanced.

More specifically, this method is outlined as follows:

1. General Considerations

Adaptive Skeleton Navigation method is developed to detect the currentlocation of a locatable guide (“LG”) being introduced through apatient's bronchial airway on a map of the bronchial tree obtained usinga CT Scan. This is achieved by constant and adaptive correlation betweenthe two data sets: the bronchial airway tree map and the sensor datahistory. The correlation above is performed via two steps:

1) Adaptive Skeleton-based Registration.

2) Adaptive Skeleton-based Navigation.

Note that these steps, described in detail below, may be performedrecursively.

2. The Adaptive Skeleton-Based Registration Algorithm

This section includes the description of the proposed algorithm ofadaptive skeleton-based registration. Registration, generally, is amethod of computing transformations between two different coordinatesystems. Here, the goal is to register the bronchial tree (BT) skeletonto the locatable guide (LG) path.

2.1 Requirements:

2.1.1 The registration accuracy improves as the locatable guide getscloser to the lower levels of the lumen network (e.g. bronchial tree)and the peripheral target.

2.1.2 The registration is updated continuously and adaptively, dependingon the location of the LG in the bronchial tree. The LG path is ahistory of LG locations as the LG is manipulated through the bronchialtree.

2.2 Technical issues:

2.2.1 Geometrically paired 3D/3D points (or other objects) from the BTskeleton and the LG path are the registration basis (see, e.g. pairs1-1′ and 2-2′ in FIG. 1).

2.2.2 The registration is continuous and adaptive. “Continuous” meansthat the registration is continually (iteratively) re-computed as new LGpath points are obtained. “Adaptive” means that different paired points,weights, and registration methods are used as the LG advances towardsthe target.

2.2.3 The registration consists of two main phases: global rigidregistration followed by local deformable registration. Deformableregistration is only performed in the lower levels of the bronchial treeand near the peripheral target. The idea is to start with rigidregistration when the bronchus is wide and switch to constrained andlocalized deformable registration when the diameter of the probe isclose to the diameter of the bronchus and the bronchus becomes flexible.

2.2.4 Global rigid registration is performed with the sophisticatedWeighted Iterative Closest-Point (WICP) method with outlier removal. Thepairing is performed by using the weighted function of position distanceand orientation difference of the paired objects. The optimizationfunction is the weighted sum of paired point distances. The parametersto determine are the weight function and the number of points to use.

2.2.5 Local non-rigid registration is performed with a constrainedelastic registration method in which paired points are connected withsprings and the optimization function is the springs' potential energy.

2.2.6 The LG path is not monotonic therefore it should be judiciouslysampled and windowed so that the path data is of good quality.

2.2.7 The accuracy or the registration improves when user-definedlandmark points are acquired with the LG.

3. Registration Algorithm

3.1 Input: BT path from CT scan, initial registration guess, LGlocations (stream)

3.2 Output: Rigid registration (6 parameters)+local deformation map

3.3 Algorithm Method—FIG. 1 shows an outline of the actual bronchus 100,with a line showing the BT skeleton 110 and a second line 120 showingthe path of the LG 115 through the bronchus 100. The path 120 of the LG115 will be used to register the BT skeleton 110 to the actual bronchus100, such that the BT skeleton, seen by the physician, is an accuraterepresentation of the actual bronchus location. The LG path 120 includesa plurality of actual LG locations 130. Corresponding projected points140 are shown on the BT skeleton 110. The differences between the actualpoints 130 and the projected points 140 are represented with lines (e.g.d1, d2) between the skeleton 110 and the path 120. With continuedreference to FIG. 1, the registration algorithm is described:

3.3.1 Perform first registration with an initial registration guess.Apply transformation to the BT. Use the registration results, obtainedfrom the initial registration phase.

3.3.2 While enroute to the target:

3.3.2.1 Obtain new stream of LG locations from sensor.

3.3.2.2 Perform cleaning, decluttered and classification (weighting) onthe stream of LG locations.

3.3.2.3 Perform selection of the LG location stream according to theoptimization decision and registration history.

3.3.2.4 Project the selected LG location segments/points on BT skeletonto obtain paired segments/points. The projection is performed byoptimizing the following criteria.

3.3.2.4.1 Minimal Distance relative to the local bronchi diameter.

3.3.2.4.2 Minimal Orientation difference.

3.3.2.4.3 The matched branch points (p1, p2, etc.) on the path.

3.3.2.5 Global rigid registration: weight paired points and obtain newrigid registration with WICP. Apply new computed transformation to theLG.

3.3.2.6 Local deformable registration: when appropriate (only when theExtended Working Channel—EWC and bronchus diameters are close to eachother), perform deformable registration on chosen window. Applytransformation to the BT local branches. The usefulness of thiscorrection shall be determined empirically.

3.3.3 Validation that the registration doesn't get worse as a result ofnoise sensor data:

3.3.3.1 Perform LG history classification by breathing averaging orspecific phase. Define the maximal deviation from the initialregistration.

3.3.3.2 Make sure that the LG history is mostly inside the bronchus.

4. The Adaptive Skeleton-Based Navigation

4.1 The basic idea

The implementation of navigation shall be similar to the navigation withthe map. Similar tasks have been implemented in GIS (GeographicInformation System) systems, such as PDA (Personal Digital Assistant)systems for blind people. It has been proven by that data topology isimportant for higher navigation accuracy. However our problem issignificantly different from above due to the bronchial tree flexibilityand movement.

4.2 The needed input information

4.2.1 Current sensor position and orientation data

4.2.2 The history of sensor position and orientation data

4.2.3 The registration (matrix) history.

4.2.4 The sources of navigation uncertainty

FIG. 2 is a table that presents the sources of error uncertainty in theorder of importance, with the values having a higher contribution intothe final error at the top of the table. The error prediction modelbased on this table shall be developed to predict the navigationuncertainty. The assumed prediction model is the sphere around thecomputed location whose radius includes the localization uncertainty.The radius of this sphere is a function of time and location.

In Use

FIGS. 3-13 show several stages of the aforementioned methods. First, thepathway generation process is described. FIG. 3 is a chart showing thepathway generation steps 10-18. FIGS. 4-13 are representations of theuser interface that guides a user through these steps during theplanning stage prior to a procedure.

A user engaged in the first step 10, adding a target, is shown in FIG.4. The upper left quadrant is a CT cross-section reconstructed from theviewpoint of the patients feet looking toward his head. The lower leftquadrant is a CT cross-section reconstructed from the front of thepatient such that the plane is parallel to the table on which thepatient is lying. The upper right quadrant is a cross-sectionreconstructed from CT directed at the side of the patient. Cross-hairsmark the targeted spot that is the intersection of all thecross-sections reconstructed from CT. Each of the CT cross-sectionsshows a projection of a small camera that represents the virtualbronchoscope inside the CT volume. The lower right quadrant is a virtualbronchoscopy view of the targeted spot from within the BT as though seenfrom the camera. Notably, the display shown in the Figures is just anexample. If the user feels the need for different views, the systemallows for views from any angle to be displayed in the variousquadrants. Hence, the system is completely configurable and customizableto the user's preferences.

In FIG. 5, the user has selected the target and the message “Crosshairspositioning” appears in the user messages box in the upper right cornerof the user interface. Selecting the very bottom button 20, marks thetarget, as seen in FIG. 6. The user may enter a target name in the box22, thus beginning the next step of FIG. 3.

FIG. 7 illustrates that the user has the option of displaying the BTskeleton in the upper right quadrant, rather than the side view.

In FIG. 8, the selected target is being measured. In FIG. 9, a name hasbeen assigned to the target.

The pathway creation step 14 begins in FIG. 10. The pathway button 24 ispressed and a name is given to the pathway.

FIG. 11 shows the exit point marking step 16. The exit point is markedon the map, from which a straight line will be drawn to the target.Having identified a destination (exit point), the pathway can bedetermined.

Next the add waypoints step 18 is completed, as seen in FIGS. 12 and 13.Waypoints may be added to assist the user to follow the pathway bymarking the turns enroute to the exit point.

FIG. 14 shows the user interface during a procedure being conducted on apatient. In the upper right quadrant, the three-dimensional BT skeletonis shown with an LG indicator 30 visible. The aforementioned ANalgorithm ensures that the BT skeleton remains registered with thepatient.

Although the invention has been described in terms of particularembodiments and applications, one of ordinary skill in the art, in lightof this teaching, can generate additional embodiments and modificationswithout departing from the spirit of or exceeding the scope of theclaimed invention. Accordingly, it is to be understood that the drawingsand descriptions herein are proffered by way of example to facilitatecomprehension of the invention and should not be construed to limit thescope thereof. Additionally, it should be noted that any additionaldocuments referenced in the attached documents are incorporated byreference herein in their entireties.

1-14. (canceled)
 15. A method of generating an intra-lumen pathway to atarget inside the body, comprising: imaging a patient to obtain aplurality of scans; creating a 3D image volume from the scans comprisinga plurality of voxels; selecting a target in the 3D image volume;detecting an anatomical feature related to the network of lumens withinthe 3D image volume; segmenting only voxels that represent space insidethe lumens of the network; generating a computerized three-dimensionalmodel of the lumen network using the segmented voxels; marking a pathwayto the target on the three-dimensional model of the lumen networkmarking an exit point of the pathway on the three-dimensional model ofthe lumen network; and directly connecting the exit point and the targetwith a single straight line.
 16. The method of claim 15 whereindetecting an anatomical feature related to the network comprisesdetecting a trachea.
 17. The method of claim 16 wherein detecting thetrachea includes searching for a tubular object in the lumen networkhaving a density of air in the upper region of the network.
 18. Themethod of claim 15 wherein detecting an anatomical feature related tothe network includes detecting an aorta.
 19. The method of claim 15wherein segmenting only voxels that represent space inside the lumens ofthe network includes marking voxels that represent a substance containedwithin the lumens of the network.
 20. The method of claim 19 whereinsegmenting only voxels that represent a substance inside the lumens ofthe network further includes avoiding bubbles caused by imaging noiseand artifacts.
 21. The method of claim 15 wherein segmenting only voxelsthat represent space inside the lumens of the network includes: a)defining a threshold value; b) defining a seed point at a center pointinside of the anatomical feature related to the network; c) marking allrelated voxels according to the threshold value beginning with the seedpoint; d) recording a whole number of marked voxels; e) increasing thethreshold value; f) repeating steps c) and d), thereby beginning a nextiteration; g) comparing the number of marked voxels to the numberdetermined in a previous iteration; h) setting a limit for the increasedetermined at g) over which the increase is attributed to leakage andthe increased threshold value at e) is reset to that from the previousiteration, thereby establishing a selected threshold; i) segmenting themarked voxels using the selected threshold; j) adding the segmentedvoxels to the seed point, thereby growing a pathway and resetting theseed point to a center of a distal end of the pathway; and k) repeatingsteps c) through j) until the pathway is complete.